1. Field of the Invention
The present invention relates to artificial neural networks, and more specifically relates to a dynamic synapse for an artificial neural network.
2. The Prior Art
Several schemes for using a matrix of electronic devices for neuron network applications have been proposed. To date, all such schemes involve using "weights" to control the amount of current injected into an electrical node "neuron". These weights are set by controlling the value of a resistor or the saturation current of a transistor. The limitation of any such scheme is that the value of any parameter of an electronic device in an integrated circuit is not well controlled. For example, the saturation currents of two MOS transistors of the same size can be different by a factor of 2 if these devices are operated in the sub-threshold regime. The "training" mechanism that adjusts the weights must take these uncertainties into account by iterating and testing the outcome of the weight-adjustment process. It is therefore desireable that an adaptive mechanism be found whereby the matrix element adjusts itself to any uncertainty in device parameters, as part of the training process.